Distributed solar energy storage and charging system based on high-precision IoT smart meter search and measurement of electric energy parameters

Abstract

The present invention discloses a distributed photovoltaic storage charging and discharging system based on electric energy parameter search and measurement high-precision Internet of Things smart meters, comprising a first smart meter and a second smart meter, both of which adopt electric energy parameter search and measurement high-precision Internet of Things smart meters, for accurately measuring the electric energy supplied to loads by smart grids and distributed photovoltaic inverter power generation systems; the electric energy parameter search and measurement high-precision Internet of Things smart meters comprises an SVM search synthesizer, the SVM search synthesizer is used to process the measured single-phase AC transient voltage and single-phase AC transient current respectively, and obtain the Gaussian window function high-precision sine wave and its pulse number of the measured single-phase AC transient voltage and single-phase AC transient current, the output of the SVM search synthesizer enters a ratio difference calibration unit for electric energy identification and signal calibration, the output voltage of the ratio difference calibration unit enters a wavelet transform module, identifies harmonics and interharmonics, and obtains voltage and current signals of harmonics and interharmonics, so that the metering result is more accurate.

Description

Technical Field

[0001] The present invention belongs to the field of power transmission and distribution of an Internet of Things (IoT) smart grid, and relates to a distributed solar energy storage and charging / discharging system based on electric energy parameter search and measurement of a high-precision IoT smart meter. Background Art

[0002] Environmental, safety and reliable compliance make the cost optimization of traditional power generation unsustainable. The variability of renewable energy and distributed power generation costs and the compliance of electric energy make them sustainable. Renewable energy and distributed energy have ushered in the spring of development. Renewable energy and distributed energy will gradually replace most traditional energy. In the next 30 years, renewable energy and traditional power generation will be mixed to form the green smart grid era of the Internet of Things, interconnection, and smart grid. Photovoltaic (wind power) distributed power generation is the most cost-effective green energy of renewable energy. As photovoltaic (wind power) distributed power generation plays an increasingly important role in smart grids, smart grids will rely on and use photovoltaic (wind power) distributed power generation to compensate for the shortage of traditional main power. Thousands of photovoltaic (wind power) distributed power generation surplus electricity will be connected to the grid for sale. Existing technologies face the following major problems that cannot be solved:

[0003] (1) Due to the inconsistency and indirectness of environmental factors that restrict photovoltaic (wind power) distributed power generation, it is not possible to provide stable, continuous and reliable power supply and sales. Even if a battery photovoltaic storage and charging and discharging system is used, it can only be used as a short-term emergency power supply. In order to scientifically predict the demand for power generation, power sales and power consumption, so that hybrid power can be uninterrupted and reliably and stably supplied, the identities of multiple power generation, power supply, power consumption and end users cannot be intelligently identified, and the reliability and accuracy of power data are poor. At present, there is no reliable device that can overcome the above defects.

[0004] (2) A large amount of low-voltage and low-cost photovoltaic (wind power) distributed generation surplus power is connected to the grid. The existing smart meters and electricity metering systems provide integrated power supply information and data for power companies (assuming they are power sales departments). This integrated distributed data. The existing exposed distributed energy communication network is unsafe. Power companies cannot identify and segment data information and cannot control the massive terminals under the Internet of Things. The data without confidentiality is easily tampered with under the drive of interests. The data of photovoltaic (wind power) distributed generation surplus power connected to the grid is more distributed. The power sales data received by power companies cannot judge the correctness, accuracy and errors of the data, and the dispatch and prediction of electricity caused by this will lead to power outages. Therefore, in practice, power companies cannot fully realize the business of photovoltaic (wind power) distributed generation surplus power connected to the grid, which also causes the continued and large-scale promotion and application of photovoltaic (wind power) distributed generation.

[0005] (3) With the continuous access of new energy sources and new loads such as wind and solar in the existing Internet of Things + smart grid, unstable energy sources bring more high dynamic changes in current to the power grid, distortion of steady-state harmonics brought by traditional nonlinear loads, and a large number of impacts that make the three-phase balance of the power grid, harmonics, interharmonics, and voltage and current change drastically. The complex characteristics directly affect the accuracy of existing electric energy measurement, making it impossible for electric energy meters designed based on sinusoidal circuit power theory or traditional non-sinusoidal circuit power theory to truly reflect the electric energy absorbed from the power system. For example, electric energy meters based on sinusoidal circuit power theory cannot theoretically measure the active and reactive power of harmonics and interharmonics in impact loads, so there are large errors in total active and reactive power. Electric energy meters based on non-sinusoidal circuit power theory can theoretically measure the active power of harmonics and reactive power of harmonics of the same order, but still cannot measure the active power and reactive power of interharmonics, and cannot measure the reactive power between voltages and currents of different frequencies, so there are also large errors in total active and reactive power.

[0006] (4) Countermeasures for intermittent renewable energy. Using energy storage to supplement insufficient electricity is an effective method in the short term, but it cannot solve the real-time and online metering of smart grid electricity. Countermeasures for renewable energy such as wind and solar storage and charging and discharging. Engineering technicians and scholars have studied, for example, Fourier transform for harmonic and interharmonic parameter estimation, which cannot avoid spectrum leakage and grid rot effects, and has high requirements for synchronous sampling and frequency resolution. The use of existing support vector machines and neural networks requires a large number of samples, large amount of calculation, unknown frequency, and poor real-time performance.

[0007] As the basic unit of the global energy Internet, the idea of ​​integrating the Internet, the Internet of Things and the smart grid shows great growth potential. It will build a new power system with zero carbon emissions led by new energy, develop a smart meter that can perform real-time, online high-precision measurement and identify different loads and electric energy IoT, and meet the requirements of renewable energy distributed photovoltaic storage and charging systems for friendly access to smart grids, the Internet of Things, and electric energy scheduling and prediction, which will have huge economic benefits. Summary of the invention

[0008] The purpose of the embodiments of the present invention is to provide a distributed photovoltaic storage and charging and discharging system based on high-precision Internet of Things smart meters for searching and measuring electric energy parameters, so as to solve the problem that the electric energy category and the electric identity of the terminal user in the distributed photovoltaic storage and charging and discharging system of the current smart grid cannot be intelligently identified, the electric energy transmission data is integrated metering data and cannot be divided into specific power generation and power consumption individuals, the current poor encryption of power data makes the power sales, power consumption, and power generation data easy to be tampered with, the problem that the distributed photovoltaic storage and charging and discharging system surplus power cannot be accurately connected to the Internet in large quantities, the problem that the power company cannot control the terminal users and cannot accurately predict and dispatch the surplus power to other areas of the smart grid, the problem that the existing smart meters cannot measure the active and reactive electric energy of harmonics and interharmonics, and cannot measure the active and reactive electric energy between voltages and currents of different frequencies, and the problem that the total active and reactive electric energy measurement errors of the existing smart meters are large and cannot adapt to the problem of accurate and high-precision online and timely electric energy measurement of the smart grid.

[0009] The first technical solution adopted by the embodiment of the present invention is: an SVM search synthesizer, comprising:

[0010] The orthogonal signal generator is used to perform orthogonal decomposition of the input single-phase AC transient voltage / current to obtain two mutually perpendicular voltage components / current components;

[0011] A first encoder, used for orthogonally encoding two mutually perpendicular voltage components / current components;

[0012] A first measurement filter is used to filter and measure parameters of two mutually perpendicular voltage components / current components after orthogonal encoding to obtain amplitudes, phases and frequencies of the two mutually perpendicular voltage components / current components;

[0013] A compensation module, used for compensating two mutually perpendicular voltage components / current components filtered by a first measurement filter using a reference value of the same frequency;

[0014] A first transmission filter, used for performing transmission filtering on two mutually perpendicular voltage components / current components output by the compensation module;

[0015] An amplitude and phase detection and judgment module is used to judge whether the amplitude and phase of two mutually perpendicular voltage components / current components filtered and output by the first transmitting filter meet the standards;

[0016] an integrator, when the amplitude and phase detection judgment module determines that the amplitude and phase of the two mutually perpendicular voltage components / current components output by the first transmit filter do not meet the standard, for integrating the two mutually perpendicular voltage components / current components output by the first transmit filter as inputs, so that the phase and amplitude of the voltage / current obtained after the integration meet the standard;

[0017] The analog-to-digital converter is used to perform analog-to-digital conversion on the output voltage / current of the integrator, and output the converted digital signal in two paths, I and Q; or perform analog-to-digital conversion on two mutually perpendicular voltage components / current components that meet the standards and are output by the amplitude and phase detection and determination module, and define the two converted digital signals as two paths, I and Q;

[0018] A digital signal processing module is used to process the I and Q outputs of the analog-to-digital converter and then perform fourteen-level interpolation fitting on the two outputs to form a standard high-precision sine wave;

[0019] The frequency, amplitude and phase search module searches the frequency, amplitude and phase of the standard high-precision sine wave output by the digital signal processing module based on the interharmonic parameter estimation method of the support vector machine and the TLS-ESPRIT algorithm to obtain the frequency, amplitude and phase of the standard high-precision sine wave.

[0020] Furthermore, the compensation module includes:

[0021] A first comparator is used to compare one of the voltage components / current components filtered by the first measurement filter with a reference value cos 2πft having the same frequency as the voltage component / current component, and to compensate the voltage component / current component output by the first measurement filter;

[0022] A second comparator is used to compare another voltage component / current component output by the first measurement filter with a reference value -sin2πft having the same frequency as the other voltage component / current component output by the first measurement filter, and compensate the voltage component / current component output by the first measurement filter;

[0023] A first synthesizer, used for synthesizing the outputs of the first comparator and the second comparator to obtain a synthesized voltage / current;

[0024] a second encoder for encoding the output of the first synthesizer, i.e., the synthesized voltage / current;

[0025] A second orthogonal signal generator is used to perform orthogonal decomposition on the encoded composite voltage / current to obtain two mutually perpendicular voltage components / current components corresponding to the output of the first orthogonal signal generator;

[0026] a third comparator, used for comparing one of the voltage components / current components output by the second orthogonal signal generator with a reference value cos 2πft having the same frequency as the voltage component / current component output by the second orthogonal signal generator, and compensating one of the voltage components / current components output by the second orthogonal signal generator;

[0027] The fourth comparator is used to compare the other voltage component / current component output by the second orthogonal signal generator with the reference value cos 2πft having the same frequency as the other voltage component / current component output by the second orthogonal signal generator, and compensate for the other voltage component / current component output by the second orthogonal signal generator.

[0028] Furthermore, the digital signal processing module includes:

[0029] Phase oscillation register, used to perform phase correction and register on the I and Q outputs of the analog-to-digital converter ADC;

[0030] The 01 register is used to store the digital signal after the phase oscillation register has been phase corrected by 01;

[0031] High-pass filter HPF, used to perform high-pass filtering on the I and Q outputs of register 01;

[0032] A low-pass filter LPF is used to perform low-pass filtering on the I-channel and Q-channel outputs of the high-pass filter HPF;

[0033] I-channel register, used to store the output of the low-pass filter LPF of I-channel;

[0034] The Q-path register is used to register the output of the low-pass filter LPF of the Q-path;

[0035] An I-channel mapping module is used to represent the 0 in the digital signal composed of 0 and 1 stored in the I-channel register with a space and the 1 with a unit pulse, so as to obtain an I-channel mapping waveform;

[0036] A Q-path mapping module is used to represent 0 in a signal composed of 0 and 1 stored in a Q-path register with a space and 1 with a unit pulse, so as to obtain a Q-path mapping waveform;

[0037] A 0-value filling module is used to fill the I-channel mapping waveform and the Q-channel mapping waveform with 0 values ​​to obtain an I-channel 0-value filling waveform and a Q-channel 0-value filling waveform;

[0038] A second transmit filter is used to perform transmit filtering on the I-channel zero-value filling waveform and the Q-channel zero-value filling waveform;

[0039] The first sampling filter is used to perform fourteen-level interpolation on the I-channel zero-value filling waveform and the Q-channel zero-value filling wave to obtain a dense I-channel interpolated fourteen-level discrete sine wave and a Q-channel interpolated fourteen-level discrete sine wave;

[0040] The second measurement filter is used to perform measurement filtering on the input I-channel interpolated fourteen-level discrete sine wave and the Q-channel interpolated fourteen-level discrete sine wave;

[0041] The second sampling filter is used to perform fourteen-level interpolation fitting on the I-channel interpolated fourteen-level discrete sine wave and the Q-channel interpolated fourteen-level discrete sine wave output by the second measurement filter to form a standard high-precision sine wave.

[0042] Furthermore, the SVM search synthesizer further comprises:

[0043] An encryption module, used for performing 14-level step encryption on the standard high-precision sinusoidal signal output by the second sampling filter;

[0044] The SVM wave generation module is used to generate waves according to the frequency, amplitude and phase output by the frequency amplitude phase search module to obtain a Gaussian window function high-precision sine wave and its pulse number.

[0045] The second technical solution adopted by the embodiment of the present invention is: a high-precision IoT smart meter for searching and measuring electric energy parameters, comprising:

[0046] High frequency crystal oscillators and quartz crystal oscillators are used to provide real-time clocks for the system when different frequencies are required;

[0047] Four identification and metering circuits with the same structure are used to identify and meter electric energy after taking power from the A phase line, B phase line, C phase line and neutral line of the power grid one by one;

[0048] Wherein, each identification and metering circuit comprises:

[0049] Voltage sensors and compensation circuits for accurately measuring single-phase AC transient voltages of three-phase electricity;

[0050] Current sensor, used to accurately measure single-phase AC transient current of three-phase electricity;

[0051] An SVM search synthesizer is used to process the measured single-phase AC transient voltage and single-phase AC transient current respectively to obtain Gaussian window function high-precision sine waves and pulse numbers of the measured single-phase AC transient voltage and single-phase AC transient current;

[0052] The ratio difference calibration unit is used to use the Gaussian window function high-precision sine wave of the single-phase AC transient voltage and the single-phase AC transient current to make a corresponding comparison with the standard sine waves of the single-phase AC transient voltage and the single-phase AC transient current of various categories of electric energy, and use the electric energy category corresponding to the minimum comparison error as the electric energy category currently measured to realize the category identification of the measured electric energy, and calibrate the Gaussian window function high-precision sine wave of the single-phase AC transient voltage and the single-phase AC transient current according to the minimum comparison error to make it closer to the standard sine wave of the corresponding category of the electric energy currently measured;

[0053] A high-pass filter, used for high-pass filtering the sine waves of the single-phase AC transient voltage and the single-phase AC transient current output by the difference calibration unit;

[0054] An electric energy metering unit, used for measuring electric energy using the output of the high-pass filter;

[0055] The CF pulse generating unit is used to determine whether the pulse number output by the SVM search synthesizer, i.e. the flashing frequency of the LED light at the CF end of the energy metering unit, is consistent with the pulse used for monitoring energy metering;

[0056] The meter calibration parameter unit communicates information with the ratio difference calibration unit, the electric energy metering unit and the CF pulse generating unit, and is used to calibrate the metering accuracy of the electric energy metering unit, the meter difference calibration unit and the parameters of the CF pulse generating unit;

[0057] The power and effective value metering unit is used to re-measure the electric energy before the difference calibration unit, the electric energy metering unit and the CF pulse unit calibration;

[0058] The data storage device is used to store the output data of the electric energy metering unit, the CF pulse generating unit, and the power and effective value metering unit, and is connected to the distributed IO interface of the microprocessor.

[0059] Furthermore, the high-precision IoT smart meter for searching and measuring electric energy parameters further includes:

[0060] A wavelet transform module is used to perform wavelet transform on the sinusoidal voltage signal output by the contrast difference calibration unit, identify harmonics and interharmonics, and obtain voltage signals and current signals of harmonics and interharmonics;

[0061] The frequency sorting unit is used to perform frequency sorting on the voltage and current signals of the harmonics and interharmonics output by the wavelet transform module and then calculate the average value, obtain the average value of the voltage and current signals of the harmonics and interharmonics, and send the output to the high-pass filter for high-pass filtering. After filtering by the high-pass filter, the signals are input into the electric energy metering module for harmonic and interharmonic electric energy metering, and the electric energy calculated by the high-pass filter in the electric energy metering module with the sinusoidal voltage and current signals output by the ratio difference calibration unit is summed to obtain the total electric energy to be measured.

[0062] Furthermore, the high-precision IoT smart meter for searching and measuring electric energy parameters further includes:

[0063] A standby function measurement channel, one output of the standby function measurement channel is combined with the output of the temperature sensor and output to the distributed IO interface of a microprocessor such as a DSP, so that temperature control can be performed when the standby function measurement channel is used, so that the activation of the standby function measurement channel does not affect the normal operation of the high-precision IoT smart meter for searching and measuring electric energy parameters; another output of the standby function measurement channel is output to a comparator, the comparator outputs to a modulator, the modulator is connected to the output of a phase-locked loop PLL, and the comparator and the modulator are used to modulate the signal input to the standby function test channel;

[0064] The phase-locked loop PLL is used to phase-lock the real-time clock provided by the high-frequency crystal oscillator or the quartz crystal oscillator, the output of the SVM search synthesizer in each identification and measurement circuit, and the output of the modulator.

[0065] The third technical solution adopted by the embodiment of the present invention is: a distributed solar energy storage charging and discharging system based on electric energy parameter search and measurement of high-precision IoT smart meters, comprising:

[0066] Distributed photovoltaic inverter power generation system, used for photovoltaic power generation;

[0067] BMS-Battery Management System, BMS-Battery Management System is used to intelligently manage and maintain the battery pack, and use the battery pack for charging and discharging, and is connected to the first transformer through the battery pack and AC / DC PCS subsystem connected in sequence, and the first transformer is connected to the smart grid;

[0068] Photovoltaic storage charging and discharging DC cabinet is used to conduct convergence and lightning protection of the DC output of the distributed photovoltaic inverter power generation system, and to charge the battery pack of the BMS-battery management system under the control of the intelligent control manager;

[0069] Intelligent control manager, used to manage and control the charge and discharge of battery packs of BMS-battery management system;

[0070] The metering lightning protection main distribution box is used to provide overvoltage and undervoltage protection, lightning protection and electric energy metering for the output of the distributed photovoltaic inverter power generation system. The output end of the metering lightning protection main distribution box is connected to the three-phase four-wire mains power supply;

[0071] The second smart meter uses the electric energy parameter search and measurement high-precision IoT smart meter as described above, and is set on the grid side of the access point where the metering lightning protection main distribution box is connected to the main power supply, and is used to accurately measure the electric energy supplied by the smart grid to the load.

[0072] Furthermore, the metering lightning protection main distribution box includes a third circuit breaker, an over / under voltage protection circuit, a circuit breaker, a surge protection circuit, a knife switch, a terminal block, and a first smart meter, wherein:

[0073] The first smart meter uses the electric energy parameter search and measurement high-precision IoT smart meter as described above;

[0074] One end of the third circuit breaker is connected to the AC output end of the distributed photovoltaic inverter power generation system, and the other end of the third circuit breaker is connected to one end of the over-voltage and under-voltage protection circuit; the other end of the over-voltage and under-voltage protection circuit is connected in two ways, one of which is connected to one end of the circuit breaker, the other end of the circuit breaker is connected to the surge protection circuit, and the other is connected to one end of the knife switch, and the other end of the knife switch is connected to the first smart meter via the connection terminal;

[0075] The first smart meter is connected to the three-phase four-wire mains via a three-phase composite switch, and the load side of the access point is connected to the three-phase load via a three-phase switch;

[0076] The second smart electric meter is connected to the smart grid via a second transformer.

[0077] Furthermore, the distributed solar energy storage and charging / discharging system based on electric energy parameter search and measurement of high-precision IoT smart meters also includes:

[0078] The anti-reverse current controller is used to detect the power grid to determine whether there is reverse current when the distributed photovoltaic inverter power generation system is generating electricity, and to control the system to make the reverse current meet the requirements when the reverse current exceeds the requirements;

[0079] The intelligent control manager comprises:

[0080] EMS monitoring and management host protection system, used to monitor and manage multiple BMS battery management systems;

[0081] MPPT controller, used for maximum power point tracking control;

[0082] PWM controller, used to control the inverter or rectification of the AC / DC PCS subsystem connected to the battery pack in each BMS-battery management system;

[0083] Each BMS-battery management system is also connected to the temperature monitoring system and fire protection system, including:

[0084] The temperature monitoring system is used to monitor the temperature of the battery pack;

[0085] The fire protection system is used to prevent the battery pack of the BMS-battery management system from catching fire or exploding;

[0086] The photovoltaic storage charging and discharging DC cabinet comprises:

[0087] Combiner box, used to combine the DC output of distributed photovoltaic inverter power generation system;

[0088] A first circuit breaker, used for switching the output of the combiner box;

[0089] Lightning arrester, used to control the total current between the combiner box and the smart manager;

[0090] An ammeter and a voltmeter, used to detect the voltage and current of the total current between the combiner box and the smart manager;

[0091] The second circuit breaker is used to control the total current between the photovoltaic storage charging and discharging DC cabinet and the intelligent manager.

[0092] The beneficial effects of the embodiments of the present invention are:

[0093] 1. In the interaction between smart grid and power Internet of Things and from power production, transmission and distribution to end-user power distribution and multi-carbon emission reduction measurement, tracking and trading, a low-power digital power intelligent electric carbon sensor identifier (SVM search synthesizer) is designed to form a high-precision IoT smart meter for power parameter search and measurement. Since the harmonics and interharmonics generated by different loads are different, the high-precision IoT smart meter for power parameter search and measurement is used on the load switch. The high-precision IoT smart meter for power parameter search and measurement identifies the harmonics and interharmonics generated by different loads, and then identifies the load identity or load coding. The terminal equipment (load) identity recognition and automatic information pairing are realized by combining smart meters, concentrators, and load switches. The high-precision IoT smart meter for power parameter search and measurement uses SVM search synthesizer to identify the power category (power generation, power supply, power sales, and power consumption) according to frequency, amplitude, and phase, effectively solving the problem that the power category and the power identity of the end user cannot be intelligently identified in the distributed photovoltaic storage and charging system of the current smart grid.

[0094] 2. Use electric energy parameters to search and measure high-precision IoT smart meters between each power generation and user and the smart grid, and realize independent statistics of power generation and consumption data of each power generation and user, solving the problem that power transmission data is integrated metering data and cannot be divided into specific individuals. After each power generation and user uses electric energy parameters to search and measure high-precision IoT smart meters, the identity of each power generation and user can be identified, and the surplus power of multiple distributed photovoltaic storage and charging and discharging systems can be accurately connected to the Internet in large quantities, and the end users can be accurately controlled. The surplus power can be accurately predicted and dispatched to other areas of the smart grid, solving the current problem that the surplus power of distributed photovoltaic storage and charging and discharging systems cannot be accurately connected to the Internet in large quantities, and the power company cannot control the end users and cannot accurately predict and dispatch the surplus power to other areas of the smart grid.

[0095] 3. Use SVM search synthesizer to process actual power data, perform micro-electric synthesis and transmission. External forces cannot accurately estimate actual power data, making actual power data not easily tampered with, solving the problem that the current power data encryption is poor, making power sales, power consumption, and power generation data easy to be tampered with; Use SVM search synthesizer combined with wavelet transform to realize active and reactive power of harmonics and interharmonics, and perform adaptive adaptation of voltage and power of different frequencies within the Gaussian function window, so that power parameter search and measurement high-precision IoT smart meters can measure active and reactive power between voltage and current of different frequencies, solving The problem that existing smart meters cannot measure the active and reactive electric energy of harmonics and interharmonics, and cannot measure the active and reactive electric energy between voltages and currents of different frequencies; an SVM search synthesizer is used to generate a high-precision sine wave of a Gaussian window function, and high-precision electric energy measurement is initially realized. An SVM search synthesizer is used in combination with a wavelet transform to realize the active and reactive electric energy of harmonics and interharmonics, further improving the electric energy measurement accuracy and meeting the requirements for accurate and high-precision online and timely electric energy measurement in smart grids. The problem that the total active and reactive electric energy measurement errors are large and cannot adapt to the accurate and high-precision online and timely electric energy measurement in smart grids is solved.

[0096] 4. Through the IOT system station, the SVM search synthesizer can identify and encode the energy and multi-carbon of the concentrator, power parameter search and measurement high-precision IoT smart meters, load switches, power equipment, wind power, solar power generation, biomass energy, hydropower, nuclear energy and thermal power generation, which can realize the power supply measurement and multi-carbon asset management of the whole process of power generation, transmission and distribution, end-user power consumption, smart grid, power Internet of Things, and international energy Internet, replacing a large number of current sensors and metering devices in renewable energy, Internet of Things and smart grid, improving the accuracy of power metering, and realizing wireless automatic switching of power equipment in smart factories and smart manufacturing through 5G wireless network, saving power equipment controllers, PLCs, chips and load switches in distribution systems in smart manufacturing. It realizes the online and timely matching of power generation capacity and demand for each user in the whole power process, provides real-time information and instant supply and demand balance, and provides suggestions, bills and economic costs for energy consumption information issues of producers, distribution users and end users.

[0097] 5. Online update of any monitoring variable failure, improve energy utilization and energy conservation and emission reduction, and multi-carbon quantity and price supply. By analyzing the time data of load, predict future demand based on consumer behavior and improve predictive energy management for energy supply delivery. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0099] Figure 1 It is a structural schematic diagram of a distributed solar storage and charging / discharging system based on electric energy parameter search and measurement of high-precision IoT smart meters according to an embodiment of the present invention.

[0100] Figure 2 It is a structural schematic diagram of a high-precision IoT smart meter for searching and measuring electric energy parameters according to an embodiment of the present invention.

[0101] Figure 3 It is a stepwise encrypted wave diagram of the SVM search synthesizer according to an embodiment of the present invention.

[0102] Figure 4 It is a simulation diagram of the characteristic frequency estimation value of the autocorrelation matrix of the high-precision algorithm TLS-ESPRIT for searching and measuring electric energy parameters according to an embodiment of the present invention.

[0103] Figure 5 It is a simulation diagram of the frequency search value of the high-precision algorithm TLS-ESPRIT for searching and measuring electric energy parameters according to an embodiment of the present invention.

[0104] Figure 6 It is an embodiment of the present invention that includes sine waves of harmonics and interharmonics identified by wavelet transform, high-precision sine waves of Gaussian window function, and sine waves output by a frequency sorting unit, wherein (a) is a voltage waveform diagram of harmonics and interharmonics identified by wavelet transform, and (b) is a composite signal waveform diagram of a high-precision sine wave signal of a Gaussian window function of a single-phase AC transient voltage / current and a voltage / current signal of harmonics and interharmonics. DETAILED DESCRIPTION

[0105] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0106] Example 1

[0107] The embodiment of the present invention proposes a SVM orthogonal vector search frequency phase amplitude phase lock, such as Figure 3 As shown, including:

[0108] Orthogonal signal generator, used to input single-phase AC transient voltage (V in ) / current is orthogonally decomposed to obtain two mutually perpendicular voltage components (V α and V β ) / current component, single-phase AC transient voltage (V in ) / The current is outputted by the single-phase inverter units of phase A, phase B and phase C of the three-phase alternating current;

[0109] The first encoder uses an orthogonal encoder to measure two mutually perpendicular voltage components (V α and V β ) / current components are orthogonally encoded;

[0110] The first measurement filter is used to measure the two mutually perpendicular voltage components (V α and V β ) / current components are filtered and parameter measured to obtain two mutually perpendicular voltage components (V α and V β ) / amplitude, phase and frequency of current components;

[0111] A compensation module, used for compensating two mutually perpendicular voltage components / current components filtered by a first measurement filter using a reference value of the same frequency;

[0112] A first transmission filter, used for performing transmission filtering on two mutually perpendicular voltage components / current components output by the compensation module;

[0113] The amplitude and phase detection judgment module is used to judge whether the amplitude and phase of the two mutually perpendicular voltage components / current components filtered and output by the first transmitting filter meet the standard (reference value, waveform comparison can be performed, and whether the standard is met can be judged by the coincidence rate). If not, integration processing is performed on them; if yes, ADC conversion is directly performed;

[0114] an integrator, for integrating the two mutually perpendicular voltage components / current components output by the first transmit filter as inputs when the amplitude phase detection and judgment module determines that the amplitude and phase of the two mutually perpendicular voltage components / current components output by the first transmit filter do not meet the standard, so that the phase and amplitude of the voltage / current obtained after the integration meet the standard (reference value);

[0115] The analog-to-digital converter is used to perform analog-to-digital conversion on the output voltage / current of the integrator, and output the converted digital signal in two paths, I and Q; or perform analog-to-digital conversion on two mutually perpendicular voltage components / current components that meet the standards and are output by the amplitude and phase detection and determination module, and define the two converted digital signals as two paths, I and Q;

[0116] A digital signal processing module is used to process the I and Q outputs of the analog-to-digital converter and then perform fourteen-level interpolation fitting on the two outputs to form a standard high-precision sine wave;

[0117] The frequency, amplitude and phase search module searches the frequency, amplitude and phase of the standard high-precision sine wave output by the digital signal processing module based on the interharmonic parameter estimation method of the support vector machine and the TLS-ESPRIT algorithm to obtain the frequency, amplitude and phase of the standard high-precision sine wave.

[0118] Specifically, the compensation module includes:

[0119] The first comparator is used to measure one of the voltage components (V α ) / current component, and compare it with the reference value cos2πft with the same frequency, and filter the voltage component (V α ) / current component to avoid the voltage component (V α ) / The current component is defective and affects the subsequent identification;

[0120] The second comparator is used to measure the other voltage component (V β ) / current component, and compare it with the reference value -sin2πft with the same frequency, and the voltage component (V β ) / current component to avoid the voltage component (V β ) / The current component is defective and affects the subsequent identification;

[0121] A first synthesizer, used for synthesizing the outputs of the first comparator and the second comparator to obtain a synthesized voltage / current;

[0122] a second encoder for encoding the output of the first synthesizer, i.e., the synthesized voltage / current;

[0123] A second orthogonal signal generator (not shown in the figure) is used to perform orthogonal decomposition on the encoded composite voltage / current to obtain two mutually perpendicular voltage components / current components corresponding to the output of the first orthogonal signal generator;

[0124] A third comparator is used to compare one of the voltage components / current components output by the second orthogonal signal generator with a reference value cos2πft having the same frequency, and to compensate one of the voltage components / current components output by the second orthogonal signal generator to prevent one of the voltage components / current components output by the second orthogonal signal generator from being defective and affecting subsequent identification;

[0125] The fourth comparator is used to compare the other voltage component / current component output by the second orthogonal signal generator with the reference value cos2πft of the same frequency, and to compensate for the other voltage component / current component output by the second orthogonal signal generator to avoid defects in the other voltage component / current component output by the second orthogonal signal generator that affect subsequent identification.

[0126] Specifically, the digital signal processing module includes:

[0127] Phase oscillation register, used to perform phase correction and register on the I and Q outputs of the analog-to-digital converter ADC;

[0128] The 01 register is used to store the digital signal after the phase oscillation register has been phase corrected by 01;

[0129] High-pass filter HPF, used to perform high-pass filtering on the I and Q outputs of register 01;

[0130] A low-pass filter LPF is used to perform low-pass filtering on the I-channel and Q-channel outputs of the high-pass filter HPF;

[0131] I-channel register, used to store the output of the low-pass filter LPF of I-channel;

[0132] The Q-path register is used to register the output of the low-pass filter LPF of the Q-path;

[0133] The I-channel mapping module is used to represent the 0 in the digital signal composed of 0 and 1 stored in the I-channel register with a space and 1 with a unit pulse, so as to obtain the I-channel mapping waveform, such as Figure 3 As shown;

[0134] The Q-path mapping module is used to represent the 0 in the signal composed of 0 and 1 stored in the Q-path register with a space and 1 with a unit pulse, so as to obtain the Q-path mapping waveform, such as Figure 3 As shown;

[0135] The 0-value filling module is used to fill the I-channel mapping waveform and the Q-channel mapping waveform with 0 values ​​to obtain the I-channel 0-value filling waveform and the Q-channel 0-value filling waveform, such as Figure 3 As shown;

[0136] A second transmit filter is used to perform transmit filtering on the I-channel zero-value filling waveform and the Q-channel zero-value filling waveform;

[0137] The first sampling filter (not shown in the figure) is used to perform fourteen-level interpolation on the I-channel zero-value filling waveform and the Q-channel zero-value filling wave to obtain a dense I-channel interpolated fourteen-level discrete sine wave and a Q-channel interpolated fourteen-level discrete sine wave;

[0138] The second measurement filter is used to perform measurement filtering on the input I-channel interpolated fourteen-level discrete sine wave and the Q-channel interpolated fourteen-level discrete sine wave;

[0139] The second sampling filter (not shown in the figure) is used to perform fourteen-level interpolation fitting on the I-channel interpolated fourteen-level discrete sine wave and the Q-channel interpolated fourteen-level discrete sine wave output by the second measurement filter to form a standard high-precision sine wave; it relies on measurement filtering and sampling filtering to suppress the interference of the bandwidth in the interpolated fourteen-level waveform, and after measuring the error of the filtered interpolated waveform, it uses multi-layer filtering of sampling filtering to improve the waveform accuracy and synthesize a standard high-precision sine wave.

[0140] The SVM search synthesizer further comprises:

[0141] The encryption module is used to perform 14-level step encryption on the standard high-precision sinusoidal signal output by the second sampling filter, and send the encrypted signal to the frequency amplitude phase search module, such as Figure 3 As shown, the specific operations are as follows:

[0142] For the standard high-precision sine signal output by the second sampling filter, the first rising wave of 49.80-62.25 is encrypted by vector 011; the second rising wave of 62.25-74.70 is encrypted by vector 010; the third rising wave of 74.70-87.15 is encrypted by vector 001; the fourth rising wave and falling wave of 87.15-99.60 are encrypted by vector 000; the fifth falling wave of 87.15-74.7 is encrypted by vector 001; the fifth falling wave of 74.70-62.25 is encrypted by vector 010; the sixth falling wave of 62.25-49.8 The seventh descending wave from 49.8 to 37.35 is encrypted by vector 011; the eighth descending wave from 37.35 to 24.9 is encrypted by vector 101; the tenth descending wave from 24.9 to 12.45 is encrypted by vector 110; the eleventh descending wave and rising wave from 12.45 to 0 are encrypted by vector 111; the twelfth descending rising wave from 12.45 to 24.9 is encrypted by vector 110; the thirteenth rising wave from 24.9 to 37.35 is encrypted by vector 101; the fourteenth rising wave from 37.35 to 49.8 is encrypted by vector 100.

[0143] The SVM (space vector modulation) wave generation module is used to generate waves according to the frequency, amplitude and phase output by the frequency amplitude phase search module to obtain a high-precision sine wave of a Gaussian window function and its pulse number. Each pulse emitted by the SVM wave generation module is a sine wave. The SVM wave generation module generates waves continuously to obtain a high-precision sine wave of a Gaussian window function.

[0144] AC transient voltage Vin To V α The transfer function G d (s) is:

[0145]

[0146] In formula (1), ω=θ is the single-phase AC transient voltage V in The resonant frequency of

[0147] AC transient voltage V in To V β The transfer function G β (s) is:

[0148]

[0149] When 0≤k≤2, G d (s) and G q (s) is a resonant filter that can extract the single-phase AC transient voltage V in The component of the resonant frequency ω, V q With V in have the same amplitude and a 90° lag in phase angle. When the frequency deviates from ω, |G d | and |G q |The response decreases, and the speed of decrease is related to the gain k. Therefore, when the fundamental component can pass through the orthogonal generator smoothly, a small gain k can bring better selectivity and suppression of other frequency components, so that the TLS-ESPRIT frequency estimation method can quickly and stably search for a frequency f equal to ω and enter the steady-state period in a very short time. When s=j*ω,G d =1, G q =-1, then V α =V in .

[0150] Example 2

[0151] The embodiment of the present invention provides a high-precision IoT smart meter for searching and measuring electric energy parameters, such as Figure 2 As shown, including:

[0152] High frequency crystal oscillator and quartz crystal oscillator, not limited to, for example, 4.096MHz, OSC type 32768HZ, high frequency crystal oscillator and quartz crystal oscillator are used to provide real-time clock for the system when different frequency requirements are met;

[0153] Phase-locked loop PLL, used to phase-lock the real-time clock provided by high-frequency crystal or quartz crystal, the output of modulator and SVM search synthesizer, the output of phase-locked loop PLL is sent to timing management, and the timing management output is sent to timing management and chip MCU;

[0154] Four identification and metering circuits with the same structure are used to identify and measure electric energy after taking power from the A phase line, B phase line, C phase line and neutral line of the smart grid one by one. Figure 2 In the middle, the identification metering circuit of the middle line is not expressed, and each identification metering circuit includes:

[0155] Voltage sensors and compensation circuits for accurately measuring single-phase AC transient voltages of three-phase electricity;

[0156] Current sensor, used to accurately measure single-phase AC transient current of three-phase electricity;

[0157] An SVM search synthesizer is used to process the measured single-phase AC transient voltage and single-phase AC transient current respectively to obtain Gaussian window function high-precision sine waves of the single-phase AC transient voltage and single-phase AC transient current;

[0158] The ratio difference calibration unit is used to use the Gaussian window function high-precision sine wave of the single-phase AC transient voltage and the single-phase AC transient current to make a corresponding comparison with the standard sine waves of the single-phase AC transient voltage and the single-phase AC transient current of various categories of electric energy, and use the electric energy category corresponding to the minimum comparison error as the electric energy category currently measured to realize the category identification of the measured electric energy, and calibrate the Gaussian window function high-precision sine wave of the single-phase AC transient voltage and the single-phase AC transient current according to the minimum comparison error to make it closer to the standard sine wave of the corresponding category of the electric energy currently measured;

[0159] The wavelet transform module is used to perform wavelet transform on the output of the contrast difference calibration unit to obtain the voltage and current signals of harmonics and interharmonics. The voltage / current signal waveforms of harmonics and interharmonics are shown in the figure below: Figure 6 As shown in (a), the waveform of the composite signal of the Gaussian window function high-precision sine wave signal of the single-phase AC transient voltage / current and the voltage / current signal of the harmonic and interharmonic is as follows Figure 6 as shown in (b);

[0160] A frequency sorting unit is used to perform frequency sorting on the voltage and current signals of the harmonics and interharmonics output by the wavelet transform module and then calculate the average value, obtain the average value of the voltage and current signals of the harmonics and interharmonics, and send the output to a high-pass filter for high-pass filtering, and after filtering by the high-pass filter, input the high-pass filter into the electric energy metering module for harmonic and interharmonic electric energy metering, and sum the electric energy calculated by the high-pass filter in the electric energy metering module with the sinusoidal voltage and current signals output by the ratio difference calibration unit to obtain the total electric energy to be measured;

[0161] A high-pass filter is used to perform high-pass filtering on the sine waves of the single-phase AC transient voltage and the single-phase AC transient current output by the frequency sequencing unit, as well as the voltage signal and current signal of the harmonics and interharmonics output by the frequency sequencing unit;

[0162] An electric energy metering unit is used to measure electric energy using the output of the high-pass filter, and the total electric energy measured is the sum of the electric energy of the harmonics and interharmonics and the electric energy output by the ratio difference calibration unit;

[0163] A CF pulse generating unit is used to monitor whether the pulse used for electric energy metering is consistent with the flashing frequency of the LED light at the CF end of the electric energy metering unit;

[0164] The meter calibration parameter unit communicates information with the ratio difference calibration unit, the electric energy metering unit and the CF pulse generating unit, and is used to calibrate the metering accuracy of the electric energy metering unit, the meter difference calibration unit and the parameters of the CF pulse generating unit;

[0165] The power and effective value metering unit is used to re-measure the electric energy before the difference calibration unit, the electric energy metering unit and the CF pulse unit calibration;

[0166] The data storage device is used to store the output data of the electric energy metering unit, the CF pulse generating unit, and the power and effective value metering unit, and is connected to the distributed IO interface of the microprocessor such as DSP.

[0167] The electric energy parameter search and measurement high-precision IoT smart meter of the embodiment of the present invention also includes a timing management unit, a timing management unit, a temperature sensor, a system control unit, an alarm display WDT, a clock chip RTC, a static random access memory SRAM, an input and output port GPIO / bidirectional two-wire synchronous serial bus I 2 C, infrared modulation unit, serial communication UART (serial communication UART is connected to the wireless module) unit, LCD, flash memory FLASH, JTAG communication interface debugging unit, reference voltage unit, power management unit, calendar clock unit, high-energy battery, voltage regulator, power protection detection and power-on reset unit. The system control unit is used to control the system of the meter, which is implemented by an advanced controller / microprocessor. The reference voltage unit is used to generate the reference voltage for the SVM wave module of the SVM search synthesizer and the three-phase reference voltage at the common point of the smart grid.

[0168] SVM search synthesizer, ratio difference calibration unit, high-pass filter, calibration parameter unit, and power and effective value metering units are realized by DSP and other microprocessor software. The energy metering unit is realized by using energy metering chip. The CF pulse generating unit is realized by setting LDE pulse display at the CF end of the energy metering chip. The timing management unit, timing management unit, power management unit, calendar clock unit, high-energy battery, voltage stabilizer, power protection detection and power-on reset unit are realized by MCU chip software. The MCU chip and energy metering chip are connected to the distributed IO interface of DSP and other microprocessors and system control unit. The distributed IO interface of DSP and other microprocessors is connected to the system control unit. The system control unit, alarm display WDT, output and input port GPIO / bidirectional two-wire synchronous serial bus I 2 C. Infrared modulation unit, serial communication UART (serial communication UART is connected with wireless module) unit, liquid crystal display LCD, flash memory FLASH, JTAG communication interface debugging unit and reference voltage unit are realized through system control unit.

[0169] Calibration parameter unit and chip MCU, system control unit, alarm display WDT, clock chip RTC, static random access memory SRAM, general purpose input and output port GPIO / bidirectional two-wire synchronous serial bus I 2 C, infrared modulation unit, serial communication UART (serial communication UART is connected to wireless module) unit, LCD display, flash memory FLASH, JTAG communication interface debugging unit, reference voltage unit, power management unit, power protection detection and power-on reset unit exchange information, temperature sensor and data storage output information parameters to chip MCU, system control unit, alarm display WDT, clock chip RTC, static random access memory SRAM, general purpose input and output port GPIO / bidirectional two-wire synchronous serial bus I 2 C. Infrared modulation, serial communication UART (serial communication UART is connected to the wireless module), LCD, flash memory FLASH, JTAG communication interface debugging unit, reference voltage unit, power management unit, power protection detection and power-on reset unit.

[0170] The power management unit adopts a low-power power management unit, which is connected to a voltage stabilizer and a high-energy battery. The high-energy battery supplies power to the calendar clock, and the calendar clock supplies power to the MCU chip.

[0171] The SVM search synthesizer is connected to the SVM wave generation module of the distributed photovoltaic storage charging and discharging system, and drives the single-phase inverter units of the A phase, B phase, and C phase of the independent structure with high precision and rapidity to correspond to multiple groups of switches, and inverts and converts the photovoltaic power of the distributed photovoltaic storage system. In addition, the standard sine wave with identification frequency of the Gaussian window function output by the SVM search synthesizer can be input into the wavelet transformation unit for wavelet transformation after ratio difference calibration and high-pass filtering. After the wavelet transformation, the frequency sorting unit and the integrator are sequentially input to obtain the integrated current. The frequency sorting unit and the integrator are implemented in the MCU through software, and the integrated current is used to obtain harmonics and interharmonics to synthesize active power measurement and reactive power measurement, so as to complete the forward power generation (selling) power and reverse power (buying) power measurement.

[0172] An adaptive time-frequency Gaussian function window is added, and the SVM search synthesizer is connected to the wavelet transform module. The countermeasures are that large-scale renewable energy and wind, solar and storage are connected to the smart grid (less than 10% of renewable energy and wind, solar and storage are connected to the smart grid, and the advantages of the SVM search synthesizer remain unchanged; if it exceeds, it changes), making the power harmonics and interharmonics of the smart grid complex and changeable, especially the instantaneous mutations, white noise interference and other mutations and unstable signals of power harmonics and interharmonics in the time domain and frequency domain, resulting in the original SVM search synthesizer amplitude-frequency mutation distortion and the original Gaussian window function cannot be adaptively adjusted, resulting in errors in electric energy metering and fluctuations in the charging and discharging system of the photovoltaic storage.

[0173] The wavelet transform process is as follows:

[0174] Perform orthogonal wavelet decomposition on the voltage / current f(t) output by the SVM search synthesizer:

[0175] P j-1 f(t)=P j f(t)+D j f(t); (3)

[0176] Among them, P j is the j-th scale factor of f(t), P j-1 is the j-1th level scaling factor of f(t), D j is the j-th wavelet coefficient of f(t), and is the j-th scale decomposition coefficient of f(t), is the j-1th level scale decomposition coefficient of f(t), is the j-th wavelet decomposition coefficient of f(t), is the j-1th layer wavelet decomposition coefficient of f(t), is the scaling function, Ψ j,k (t) is the wavelet basis function, h 0(n-2k) is the low-pass filter unit of the wavelet packet, h1(n-2k) It is the high-pass filter unit of wavelet packet;

[0177] Wavelet packet decomposition and reconstruction:

[0178]

[0179] in, is the wavelet packet reconstruction coefficient, and is the wavelet packet decomposition coefficient, g 0(l-2k) is the low-pass filter unit reconstructed by wavelet packet, g 1(l-2k) is the high-pass filter unit reconstructed by wavelet packet, l is the harmonic number after reconstruction;

[0180] During the sampling period, the voltage signal of the harmonic is:

[0181]

[0182] Among them, k=0, i=0 only means that the initial value of the sum calculation is 0;

[0183] During the sampling period, the harmonic current signal is:

[0184]

[0185] in, is the scale space function; is the wavelet mother function; are the coefficients of the scale function in the reconstructed voltage signal, is the coefficient of the scale function in the reconstructed current signal; is the wavelet packet transform coefficient in the reconstructed voltage signal; is the wavelet packet transform coefficient in the reconstructed current signal.

[0186] Enter the active power calculation formula The expression of power P is:

[0187]

[0188] Active energy can be expressed as:

[0189]

[0190] When the SVM search synthesizer is connected to the wavelet transform combination, an online and timely variable time-frequency window is provided. When a high-frequency signal appears, the time window of the Gaussian function sine wave will automatically narrow, and when a low-frequency signal appears, the time window of the Gaussian function sine wave will automatically widen, changing the characteristic of the local Gaussian function sine wave window being unchanged, and eliminating the problem that the local Gaussian function sine wave window cannot reflect the sudden changes of harmonic and interharmonic signals, making it difficult for the wavelet transform to accurately obtain the amplitude, frequency and phase of each harmonic and interharmonic. The time window of the Gaussian function sine wave will automatically adapt to the changes brought about by different situations, and reflect the maximum values ​​of the singularity characterizing the signal at different scales. It can reflect the sudden changes and time-varying tracking distortion of harmonic and interharmonic signals. Through signal reconstruction, the stepping effect caused by data compression is eliminated. The time domain signal in each sub-band is reconstructed by wavelet packet decomposition coefficients to realize the rapid detection, measurement, analysis, resolution and tracking of power harmonic and interharmonic parameters in each frequency band. The wavelet packet reconstruction low-wave and high-wave filter combination retains the accuracy and details of the original standard of the local signal time-frequency of the Gaussian function sine wave window, and the high-precision online and timely electric energy is obtained according to the active electric energy calculation formula.

[0191] According to the SVM search synthesizer docking wavelet transform, frequency sorting and integral current, it is easy to obtain the measurement of harmonic, intermittent active and reactive energy online, and the online resolution, analysis, detection, measurement and tracking of frequency, phase and amplitude. By tracking and sorting the frequency, it can automatically identify the frequency of different source-side power generation and grid-side and load-side power, and identify the power identity of the source-side power generation, grid-side and load-side, as well as the load identity of the load-side. It can access the wireless network to obtain the power, bill, carbon emissions and carbon comprehensive amount of wireless network meters and online virtual smart meters, and realize high-precision power metering.

[0192] A backup function measurement channel, one output of the backup function measurement channel is combined with the output of the temperature sensor and output to the distributed IO interface of a microprocessor such as a DSP, so that temperature control can be performed when the backup function measurement channel is used, so that the activation of the backup function measurement channel does not affect the normal operation of the high-precision IoT smart meter for searching and measuring electric energy parameters; another output of the backup function measurement channel is output to the comparator, the comparator outputs to the modulator, the modulator is connected to the output of the phase-locked loop PLL, the comparator and the modulator are used to modulate the signal input to the backup function test channel, and after modulation, the signal is input to the phase-locked loop PLL for phase locking.

[0193] In the embodiment of the high-precision IoT smart meter for searching and measuring electric energy parameters, the modulator is not limited to the use of second-order ∑-△ modulation, and realizes multifunctional detection modulation that is not limited to system temperature detection and modulation that is not limited to system DC voltage detection. It is not limited to the use of fourth-order ∑-△ modulation, and is not limited to system DC current detection modulation. It combines digital filtering + high-pass filtering to realize the measurement of DC forward power generation (selling) and reverse power (buying) electricity. It is not limited to the use of third-order ∑-△ modulation, and is not limited to multifunctional detection modulation for system temperature detection, and is not limited to modulation for system AC voltage detection, and is not limited to system AC current detection modulation. It combines digital filtering + high-pass filtering to realize the measurement of AC forward power generation (selling) and reverse power (buying) electricity; it is not limited to the use of third-order ∑-△ modulation, and is not limited to multifunctional detection modulation for system temperature detection, and is not limited to modulation for system AC voltage detection, and is not limited to modulation for system AC current detection, and is combined with digital filtering + high-pass filtering to realize the measurement of AC forward power generation (selling) and reverse power (buying) electricity; it is not limited to the use of third-order ∑-△ modulation to respectively connect to the output A phase, B phase, C phase and neutral line of the inverter of the distributed photovoltaic inverter power generation system, and is not limited to the use of shunt-type third-order ∑-△ current modulation with a 3kHz signal bandwidth to provide a 67dB signal-to-noise ratio, and is not limited to the use of voltage-dividing third-order ∑-△ voltage modulation with a 3kHz signal bandwidth to provide a 72dB signal-to-noise ratio. The distributed photovoltaic inverter power generation system supplies power to the load user as forward 1, the distributed photovoltaic inverter power generation system supplies power to the grid as forward 2, the distributed photovoltaic inverter power generation system supplies power to the BMS-battery system (BMS battery pack) as forward 3, the BMS-battery system supplies power to the load user (not shown in the figure) as forward 4, the BMS-battery system discharges power to the grid as forward 5, the BMS-battery system supplies power to the distributed photovoltaic inverter power generation system and control system (such as temperature control system, fire protection system) as forward 6, and the BMS-battery system supplies power to the distributed photovoltaic inverter power generation system. The corresponding reverse connection system is required to realize ( Figure 1 The grid supplies power to the distributed photovoltaic inverter power generation system (needs to connect to the corresponding reverse connection system, Figure 1 The power supply from the power grid to the load user is defined as negative 1, the power supply from the power grid to the BMS-battery system is defined as negative 3; the positive (reverse) voltage and current provided by the voltage-dividing three-order ∑-△ voltage modulation and the current-dividing three-order ∑-△ voltage modulation are provided to the multiplier of the electric energy metering unit for operation, and the positive and negative active energy, combined active energy, multi-quadrant reactive energy, combined reactive energy, apparent energy, phase-splitting energy and rate-splitting energy are calculated. The electric energy metering unit, real-time buying and selling electricity, and real-time clock are controlled by the MCU chip (metering chip). The frequency output by the MCU chip is connected to the microprocessor such as DSP through the SPI interface. The LCD display, infrared communication, RS485 communication, Bluetooth communication, carrier communication, module communication, data storage, alarm output, key input, magnetic field detection, real-time detection, low-power power management, relay control and control module function settings are arranged around the MCU.

[0194] Example 3

[0195] The embodiment of the present invention proposes a method for estimating interharmonic parameters using a support vector machine and a TLS-ESPRIT algorithm to solve the phase, amplitude and frequency of single-phase alternating current. The specific process is as follows:

[0196] The sampling real signal expression of grid carbon is:

[0197]

[0198] Among them, x(n) is the real signal sampled by the power grid at the nth sampling point, p is the number of harmonic components of the real signal sampled by the power grid, and EF k is the kth carbon emission coefficient, α k is the amplitude of the kth harmonic and interharmonic components of the real signal sampled by the power grid, ω k is the angular frequency of the kth harmonic and interharmonic components of the grid sampling real signal, is the phase of the kth harmonic and interharmonic components of the real signal sampled by the power grid, and ω(n) is the noise component of the real signal sampled by the power grid at the nth sampling point.

[0199] Based on TLS-ESPRIT, the grid frequency is solved and formula (9) is transformed into a sampled complex signal through Euler transformation, that is, formula (10):

[0200]

[0201] Among them, α′ k is the amplitude of the kth harmonic and interharmonic components of the sampled complex signal, ω′ k is the angular frequency of the kth harmonic and interharmonic components of the sampled complex signal, is the phase of the kth harmonic and interharmonic components of the sampled complex signal.

[0202] Define an L×1 (L>>2p) dimensional semaphore:

[0203] X(n)=[x(n),x(n+1),…,x(n+L-1)] T ; (11)

[0204] Using formula (10), formula (11) can be described as:

[0205]

[0206] In the formula, A=[α(ω′1),α(ω′2),…,α(ω′ 2P )], W(n)=[W(n), W(n+1),…, W(n+L-1)] T ;

[0207] In formula (12), when 1≤k≤p,

[0208] When p≤k≤2p, ω′ k =-ω k-p , a k-p is the amplitude of the kpth harmonic and interharmonic components of the real signal sampled by the power grid, ω k-p is the frequency of the kpth harmonic and interharmonic components of the real signal sampled by the power grid, is the initial phase angle of the kpth harmonic and interharmonic components of the real signal sampled by the power grid;

[0209] Remove the first row S(n) and the last row, and use the vertical decomposition method to obtain the intersecting vectors S1 and S2 respectively:

[0210]

[0211] set up The frequency information of the signal is completely contained in the rotation factor matrix middle.

[0212] Sampling data to form a time series:

[0213] Based on the constraint of minimum overall mean square error, the frequency parameters of interharmonics are estimated. The process is as follows:

[0214] (1) Construct the HANKEL matrix using sampling data:

[0215]

[0216] Where M is the number of array elements, M>L>>2p, M+L-1=N;

[0217] (2) Perform singular value decomposition on matrix X:

[0218]

[0219] Where L is the left singular vector matrix, U H is the right singular vector matrix; ∑ is the diagonal matrix of singular values ​​arranged in descending order; L s is the left singular vector matrix corresponding to the maximum singular value, L n is the left singular vector matrix corresponding to the minimum singular value; is the right singular value vector matrix corresponding to the 2P largest singular values, ∑ s yes The span signal subspace of is the right singular value vector matrix corresponding to the L-2p smallest singular values, ∑ n yes The spanned noise subspace.

[0220] (3) Remove The first and last rows of , using the vertical decomposition method, are used to obtain two intersecting vectors U1 and U2, respectively. Let U1 = ΨU1, and use the least squares idea to perform singular value decomposition on the matrix [U1, U2]:

[0221]

[0222] (4) The matrix is ​​decomposed into 4 2p×2p square matrices:

[0223]

[0224] Then we have:

[0225]

[0226] (5) For Ψ TLS Perform eigenvalue decomposition and obtain the eigenvalue λ k , eigenvalue λ k That is the rotation factor matrix The diagonal elements of , from which the frequency parameters of the signal are estimated:

[0227]

[0228] Amplitude and phase search based on support vector machines:

[0229] Transforming formula (9), we get:

[0230]

[0231] In the formula, The noise component ω(n) is the model error e at the nth signal sampling point n , then the amplitude of the signal α k and Phase Can be C K and D K Find:

[0232]

[0233] Let W = [C1, ...C p , D1,…D p ], select the quadratic ε-insensitive loss function, introduce the Lagrange function to the optimization problem, and get the standard iterative variable weighted least squares format that is only related to W:

[0234]

[0235] Where, L W is the minimum point of W, λ n =2α n (e n -ε), is the Lagrange multiplier, which can be obtained by the KKT condition; Find:

[0236]

[0237] In the formula, The diagonal elements are The diagonal matrix of The diagonal elements are A diagonal matrix of; X is a column vector composed of sampled data, y n =[cosω1n,…,cosω p n, -sinω1n, ..., -sinω p n], ε is a column vector whose elements are all ε.

[0238] After obtaining W, the amplitude and phase of the signal can be obtained from C K and D K It is obtained according to formula (21). It avoids searching for frequency, amplitude and phase in a large range and reduces the computational complexity of the support vector machine algorithm.

[0239] Implementation verification:

[0240] 1. TLS-ESPRIT frequency search simulation (compared with traditional autocorrelation matrix eigenfrequency estimation)

[0241] The sampling frequency is 1000 Hz, the number of samples is 1000 points, c = 0.5; ε = 0.01; ω is Gaussian white noise with SNR = 20 dB.

[0242] Depend on Figure 4 The estimated eigenfrequencies of the autocorrelation matrix are 50.00048, 8546954, and 150.0358. Figure 5 Given TLS-ESPRIT frequency search values: 50.0000; 85.0001, Figure 4 and Figure 5 The comparison shows that in the case of low signal-to-noise ratio, the accuracy of interharmonic frequency search by TLS-ESPRIT is higher than that of eigenfrequency estimation by autocorrelation matrix, and TLS-ESPRIT frequency search reduces correlation calculations.

[0243] Table 1 TLS-ESPRIT frequency search value simulation signal parameter estimation results

[0244] project Frequency / Hz Amplitude / V Phase angle / (°) 1 45.00049 2.50000 19.9852 2 50.0987 99.66599 30.00498 3 115.0001 1.991598 39.9479 4 149.9999 3.98909 60.09809 5 175.00001 1.49598 44.9759

[0245] From the simulation signal parameter estimation results of the TLS-ESPRIT frequency search value in Table 1, it can be seen that the TLS-ESPRIT frequency search can accurately search and estimate the signal parameters of the smart grid with multiple harmonic and interharmonic components whose fundamental frequency is offset under low signal-to-noise ratio conditions.

[0246] 2. Simulation of phase amplitude search for support vector machine (compared with least squares LS estimation)

[0247] Table 2 Comparison of estimation results between LS and support vector machine search algorithms

[0248]

[0249] From the comparison in Table 2, we can see that under high signal-to-noise ratio, both the support vector machine algorithm and the least squares method LS have good estimation performance of amplitude and phase parameters; but under low signal-to-noise ratio, the support vector machine algorithm shows better estimation performance and has better stability.

[0250] The embodiment of the present invention combines the advantages of TLS-ESPRIT search frequency + support vector machine phase amplitude estimation + wavelet transform, and solves the problems of large amount of calculation and low accuracy when using a single algorithm, the sudden change and distortion of harmonics and interharmonics caused by large-scale access of renewable energy including wind, solar and storage to smart grid, the long chip analysis time, and the inability to meet the requirements of smart grid access standards. Especially under low signal-to-noise ratio, the accuracy is 2 levels higher than the existing method, reaching 10 -5 ~10 -3 TLS-ESPRIT accurately divides the smart grid signal space and shields the influence of noise on frequency. The calculation accuracy of TLS-ESPRIT and support vector machine phase amplitude + wavelet transform is higher than that of existing technology algorithms.

[0251] Example 4

[0252] like Figure 1 As shown, a distributed solar storage charging and discharging system based on electric energy parameter search and measurement and high-precision IoT smart meter for smart grid and IoT includes:

[0253] Distributed photovoltaic inverter power generation system, used for photovoltaic power generation;

[0254] BMS-Battery Management System, BMS-Battery Management System is used to intelligently manage and maintain the battery pack. It is connected to the first transformer (10kv / 0.4kv power transformer) through the battery pack and AC / DC PCS subsystem connected in sequence. The first transformer is connected to the smart grid. BMS-Battery Management System uses the battery pack to charge and discharge, and realizes frequency regulation, voltage regulation, emergency power support, and peak regulation of the smart grid;

[0255] The photovoltaic storage charging and discharging DC cabinet is used to conduct convergence and lightning protection on the DC output of the distributed photovoltaic inverter power generation system, and then charge the battery pack of the BMS-battery management system under the control of the intelligent control manager;

[0256] like Figure 1 As shown, the photovoltaic storage charging and discharging DC cabinet includes:

[0257] Combiner box, used to combine the DC output of distributed photovoltaic inverter power generation system;

[0258] A first circuit breaker, used for switching the output of the combiner box;

[0259] Lightning arrester, used to control the total current between the combiner box and the smart manager;

[0260] An ammeter and a voltmeter, used to detect the voltage and current of the total current between the combiner box and the smart manager;

[0261] The second circuit breaker is used to control the total current between the photovoltaic storage charging and discharging DC cabinet and the intelligent manager.

[0262] The distributed solar storage charging and discharging system based on the high-precision IoT smart meter search and measurement of electric energy parameters also includes:

[0263] Intelligent Control Manager, Intelligent Control Manager includes:

[0264] EMS monitoring and management host protection system, used to monitor and manage multiple BMS battery management systems;

[0265] MPPT controller is used for maximum power tracking control, real-time detection of the power generation voltage of the solar panel, and tracking the highest voltage and current values, so that the system can output at maximum power;

[0266] PWM controller, used to control the inverter or rectification of the AC / DC PCS subsystem connected to the battery pack in each BMS-battery management system.

[0267] Each BMS-battery management system is also connected to a temperature monitoring system and a fire protection system. The temperature monitoring system is used to monitor the temperature of the battery pack, and the fire protection system is used to prevent the battery pack from catching fire or exploding.

[0268] The distributed solar storage charging and discharging system based on the high-precision IoT smart meter search and measurement of electric energy parameters also includes:

[0269] A metering and lightning protection main distribution box connected to the distributed photovoltaic storage charging and discharging system, the metering and lightning protection main distribution box is used to perform overvoltage and undervoltage protection, lightning protection and metering on the electric energy output by the distributed photovoltaic inverter power generation system, and supply power to the load, the metering and lightning protection main distribution box includes a third circuit breaker, an over / undervoltage protection circuit, a circuit breaker, a surge protection circuit, a knife switch, a terminal, and a first smart meter. Specifically, the first smart meter adopts the electric energy parameter search and measurement high-precision IoT smart meter, and one end of the third circuit breaker is connected to the AC output end of the distributed photovoltaic inverter power generation system. The other end of the third circuit breaker is connected to one end of the over / undervoltage protection circuit, and the other end of the over / undervoltage protection circuit is connected in two ways, one of which is connected to one end of the circuit breaker, and the other end of the circuit breaker is connected to the surge protection circuit, and the other is connected to one end of the knife switch, and the other end of the knife switch is connected to the first smart meter via the connecting terminal. The first smart meter is connected to the three-phase four-wire AC power transmission line via the three-phase composite switch. A second smart meter is set on the access point network side. The second smart meter also uses electric energy parameter search and measurement high-precision IoT smart meter. The load side of the access point is connected to the three-phase load via the three-phase switch.

[0270] Furthermore, the other end of the second smart meter is connected to the smart grid via a second transformer (10kv / 0.4kv power transformer).

[0271] Furthermore, the distributed solar energy storage charging and discharging system based on the high-precision IoT smart meter search and measurement of electric energy parameters also includes:

[0272] The anti-reverse current controller is used to detect the power grid to determine whether there is reverse current in the system when the distributed photovoltaic inverter power generation system is generating electricity, and to control the system to make the reverse current meet the requirements when the reverse current exceeds the requirements. Specifically:

[0273] When the distributed photovoltaic inverter power generation system is generating electricity, if reverse current is detected, the power of the battery pack in the BMS battery management system is judged. If the battery pack in the BMS battery management system is not full, the distributed photovoltaic inverter power generation system is controlled to charge the battery pack in the BMS battery management system, and then it is judged whether the reverse current still exists. If the reverse current continues to exist, the inverter output current is controlled to be reduced until the reverse current meets the requirements; if the battery pack in the BMS battery management system is full, the distributed photovoltaic inverter power generation system output current is directly controlled to be reduced until the reverse current meets the requirements.

[0274] When the smart grid generates electricity to the distributed photovoltaic inverter power generation system, it needs to be connected to the distributed photovoltaic inverter power generation system through the corresponding reverse connection system. Specifically, the 3S / 2R conversion module is used to convert the three-phase electricity at the common point of the smart grid into the DC component V in the synchronous rotating coordinate system. d 、V q Then a phase-locked loop (PLL) is used for phase locking to output the frequency, phase and amplitude of the three-phase electricity at the common point of the smart grid. The frequency output by the phase-locked loop (PLL) is used to generate a SVM (space vector modulation) wave module to control the converter rectification.

[0275] When a phase-locked loop (PLL) is used for phase locking, the PLL includes a loop filter, a PI controller and an integrator connected in sequence. The input of the PLL is connected to a multiplier. One input of the multiplier is the three-phase power at the common point of the smart grid, and the other input is the output of the PLL. After the two inputs are multiplied by the multiplier, the following is obtained:

[0276]

[0277] Among them, the previous is the low-frequency component of the phase difference between the input and output of the phase-locked loop PLL. is the high frequency component that can be filtered out by the loop filter; ω is the frequency of the output signal of the phase-locked loop PLL, is the output stator flux signal of the phase-locked loop PLL, ω g is the frequency of the input signal of the phase-locked loop PLL, V is the excitation signal of the input signal of the phase-locked loop PLL. m is the reference voltage in the multiplier;

[0278] The input to the phase-locked loop (PLL) first enters the loop filter (LPF) of the phase-locked loop (PLL) to filter out high-frequency components. Output signal V f :

[0279]

[0280] Then the signal V f As the DC component V d The input PI controller generates an estimated frequency ω=θ after PI adjustment. The frequency is integrated to form the output signal of the PLL, realizing output phase locking of the input phase. The input signal and the output signal differ by 90°. The input signal is a cosine function, and the output signal is a sine function, so that the phase angle of the output signal plus a constant value can meet the requirements of any phase angle.

[0281] In the synchronously rotating coordinate system, V d 、Vq Two DC components, in order to lock the phase of the input signal, it is required that θ = θ g ,ω=ω g , In steady state, V d =0, the PI controller outputs the estimated frequency, the estimated frequency is integrated to obtain the estimated phase and the estimated voltage amplitude. When the phase is locked, E = V q At this time, the frequency, amplitude and phase angle can be obtained from the synchronous rotating coordinate phase-locked loop, and then the estimated frequency, phase and voltage amplitude E are used for wave generation.

[0282] The estimated frequency plays a role in phase error detection during 3S / 2R conversion. d It is a DC component, the loop filter is a simple unit gain, the PI controller and integrator will generate frequency and phase, and eliminate static errors by tracking phase and frequency, so that it can obtain fast and accurate tracking performance under high bandwidth conditions.

[0283] In order to verify the effectiveness of the distributed solar storage charging and discharging system based on the high-precision IoT smart meter search and measurement of electric energy parameters, according to Figure 1 Simplify the design of photovoltaic power matching power parameter search and measurement high-precision IoT smart meter, and verify it on a prototype of one power parameter search and measurement high-precision IoT smart meter connected to two 500KW converters in parallel. The 500KW topology structure KW maximum power tracking efficiency (MPPT) ≥ 99.9%, using space vector modulation algorithm (SVM / SVPWM), DSP control, DSP control chip TMS320F28075-Q control MTTP and PMW control, multi-directional power measurement selection Select the metering chip ADE7913, and the MCU selects the chip UPD78F1166. Under the control of hardware and software, the distributed photovoltaic storage and charging system based on the high-precision IoT smart meter based on the power parameter search and measurement realizes the photovoltaic power matching and grid-connected metering. It has the functions of forward and reverse active power, reactive forward and reverse, combined active power, multi-quadrant reactive power, combined reactive power, apparent power, phase power and rate power metering. The combination mode feature word can be set, and it has the remote fee control function (fee control smart power meter configuration). It has the function of storing historical power for 12 settlement cycles. The stored historical power includes the total and rate power data and phase power data of forward and reverse active power, combined active power, multi-quadrant reactive power, combined reactive function, forward and reverse apparent power, and adopts the current error detection of the live wire and the ground wire (neutral wire) to realize the function of anti-theft of electricity, while improving the light load efficiency, accuracy, economy and reliability.

[0284] Figure 2The electric energy parameter search and measurement high-precision IoT smart meter has the following technical parameter requirements: rated voltage 3×220 / 380; reference frequency (Hz) 50; current: 3×1.5(6); function level: active level 1, non-reactive level 2; pulse constant: active (imp / kwh, 6400), non-reactive imp / kwh, 6400.

[0285] Through the electric energy meter of the standard laboratory testing platform, the real-time data of the high-precision IoT smart meter based on the electric energy parameter search and measurement in the distributed solar storage charging and discharging system is fully automatically detected. The comparison object is the power source. The results are shown in Tables 3-6.

[0286] Table 3. Real-time positive error of active power in high-precision IoT smart meter search and measurement of electric energy parameters in the embodiment of the present invention

[0287]

[0288]

[0289] Table 4: Real-time error of active reverse of high-precision IoT smart meter in searching and measuring electric energy parameters according to the embodiment of the present invention

[0290]

[0291] Table 5: Real-time positive reactive power error of high-precision IoT smart meter measured by searching electric energy parameters according to the embodiment of the present invention

[0292]

[0293]

[0294] Table 6 Reactive reverse real-time error of high-precision IoT smart meter measured by searching electric energy parameters according to the embodiment of the present invention

[0295]

[0296] The active forward error of the high-precision IoT smart meter measured by electric energy parameter search meets the design level 1 standard, the active reverse error meets the design level 1 standard, the reactive forward error meets the design level 2 standard, and the reactive reverse error meets the design level 2 standard. This shows that the design of the distributed solar storage and charging and discharging system based on the high-precision IoT smart meter measured by electric energy parameter search is effective and meets the photovoltaic power generation and measurement standards.

[0297] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A SVM search synthesizer, characterized in that: include: The orthogonal signal generator is used to perform orthogonal decomposition of the input single-phase AC transient voltage / current to obtain two mutually perpendicular voltage components / current components; A first encoder, used for orthogonally encoding two mutually perpendicular voltage components / current components; A first measurement filter is used to filter and measure parameters of two mutually perpendicular voltage components / current components after orthogonal encoding to obtain amplitudes, phases and frequencies of the two mutually perpendicular voltage components / current components; A compensation module, used for compensating two mutually perpendicular voltage components / current components filtered by a first measurement filter using a reference value of the same frequency; A first transmission filter, used for performing transmission filtering on two mutually perpendicular voltage components / current components output by the compensation module; An amplitude and phase detection and judgment module is used to judge whether the amplitude and phase of two mutually perpendicular voltage components / current components filtered and output by the first transmitting filter meet the standards; an integrator, when the amplitude and phase detection judgment module determines that the amplitude and phase of the two mutually perpendicular voltage components / current components output by the first transmit filter do not meet the standard, for integrating the two mutually perpendicular voltage components / current components output by the first transmit filter as inputs, so that the phase and amplitude of the voltage / current obtained after the integration meet the standard; The analog-to-digital converter is used to perform analog-to-digital conversion on the output voltage / current of the integrator, and output the converted digital signal in two paths, I and Q; or perform analog-to-digital conversion on two mutually perpendicular voltage components / current components that meet the standards and are output by the amplitude and phase detection and determination module, and define the two converted digital signals as two paths, I and Q; A digital signal processing module is used to process the I and Q outputs of the analog-to-digital converter and then perform fourteen-level interpolation fitting on the two outputs to form a standard high-precision sine wave; The frequency, amplitude and phase search module searches the frequency, amplitude and phase of the standard high-precision sine wave output by the digital signal processing module based on the interharmonic parameter estimation method of the support vector machine and the TLS-ESPRIT algorithm to obtain the frequency, amplitude and phase of the standard high-precision sine wave.

2. A SVM search synthesizer according to claim 1, characterized in that: The compensation module comprises: A first comparator is used to compare one of the voltage components / current components filtered by the first measurement filter with a reference value cos 2πft having the same frequency as the voltage component / current component, and to compensate the voltage component / current component output by the first measurement filter; A second comparator is used to compare another voltage component / current component output by the first measurement filter with a reference value -sin 2πft having the same frequency as the other voltage component / current component output by the first measurement filter, so as to compensate the voltage component / current component output by the first measurement filter; A first synthesizer, used for synthesizing the outputs of the first comparator and the second comparator to obtain a synthesized voltage / current; a second encoder for encoding the output of the first synthesizer, i.e., the synthesized voltage / current; A second orthogonal signal generator is used to perform orthogonal decomposition on the encoded composite voltage / current to obtain two mutually perpendicular voltage components / current components corresponding to the output of the first orthogonal signal generator; a third comparator, used for comparing one of the voltage components / current components output by the second orthogonal signal generator with a reference value cos 2πft having the same frequency as the voltage component / current component output by the second orthogonal signal generator, and compensating one of the voltage components / current components output by the second orthogonal signal generator; The fourth comparator is used to compare the other voltage component / current component output by the second orthogonal signal generator with the reference value cos 2πft having the same frequency as the other voltage component / current component output by the second orthogonal signal generator, and compensate the other voltage component / current component output by the second orthogonal signal generator.

3. A SVM search synthesizer according to claim 1, characterized in that: The digital signal processing module comprises: Phase oscillation register, used to perform phase correction and register on the I and Q outputs of the analog-to-digital converter ADC; The 01 register is used to store the digital signal after the phase oscillation register has been phase corrected by 01; High-pass filter HPF, used to perform high-pass filtering on the I and Q outputs of register 01; A low-pass filter LPF is used to perform low-pass filtering on the I-channel and Q-channel outputs of the high-pass filter HPF; I-channel register, used to store the output of the low-pass filter LPF of I-channel; The Q-path register is used to register the output of the low-pass filter LPF of the Q-path; An I-channel mapping module is used to represent the 0 in the digital signal composed of 0 and 1 stored in the I-channel register with a space and the 1 with a unit pulse, so as to obtain an I-channel mapping waveform; A Q-path mapping module is used to represent 0 in a signal composed of 0 and 1 stored in a Q-path register with a space and 1 with a unit pulse, so as to obtain a Q-path mapping waveform; A 0-value filling module is used to fill the I-channel mapping waveform and the Q-channel mapping waveform with 0 values ​​to obtain an I-channel 0-value filling waveform and a Q-channel 0-value filling waveform; A second transmit filter is used to perform transmit filtering on the I-channel zero-value filling waveform and the Q-channel zero-value filling waveform; The first sampling filter is used to perform fourteen-level interpolation on the I-channel zero-value filling waveform and the Q-channel zero-value filling wave to obtain a dense I-channel interpolated fourteen-level discrete sine wave and a Q-channel interpolated fourteen-level discrete sine wave; The second measurement filter is used to perform measurement filtering on the input I-channel interpolated fourteen-level discrete sine wave and the Q-channel interpolated fourteen-level discrete sine wave; The second sampling filter is used to perform fourteen-level interpolation fitting on the I-channel interpolation fourteen-level discrete sine wave and the Q-channel interpolation fourteen-level discrete sine wave output by the second measurement filter to form a standard high-precision sine wave.

4. A SVM search synthesizer according to claim 1, characterized in that: Also includes: An encryption module, used for performing 14-level step encryption on the standard high-precision sinusoidal signal output by the second sampling filter; The SVM wave generation module is used to generate waves according to the frequency, amplitude and phase output by the frequency amplitude phase search module to obtain a Gaussian window function high-precision sine wave and its pulse number.

5. A high-precision IoT smart meter for searching and measuring electric energy parameters, characterized in that: include: High frequency crystal oscillators and quartz crystal oscillators are used to provide real-time clocks for the system when different frequencies are required; Four identification and metering circuits with the same structure are used to identify and meter electric energy after taking power from the A phase line, B phase line, C phase line and neutral line of the power grid one by one; Wherein, each identification and metering circuit comprises: Voltage sensors and compensation circuits for accurately measuring single-phase AC transient voltages of three-phase electricity; Current sensor, used to accurately measure single-phase AC transient current of three-phase electricity; An SVM search synthesizer is used to process the measured single-phase AC transient voltage and single-phase AC transient current respectively to obtain Gaussian window function high-precision sine waves and pulse numbers of the measured single-phase AC transient voltage and single-phase AC transient current; The ratio difference calibration unit is used to use the Gaussian window function high-precision sine wave of the single-phase AC transient voltage and the single-phase AC transient current to make a corresponding comparison with the standard sine waves of the single-phase AC transient voltage and the single-phase AC transient current of various categories of electric energy, and use the electric energy category corresponding to the minimum comparison error as the electric energy category currently measured to realize the category identification of the measured electric energy, and calibrate the Gaussian window function high-precision sine wave of the single-phase AC transient voltage and the single-phase AC transient current according to the minimum comparison error to make it closer to the standard sine wave of the corresponding category of the electric energy currently measured; A high-pass filter, used for high-pass filtering the sine waves of the single-phase AC transient voltage and the single-phase AC transient current output by the difference calibration unit; An electric energy metering unit, used for measuring electric energy using the output of the high-pass filter; The CF pulse generating unit is used to determine whether the pulse number output by the SVM search synthesizer, i.e. the flashing frequency of the LED light at the CF end of the energy metering unit, is consistent with the pulse used for monitoring energy metering; The meter calibration parameter unit communicates information with the ratio difference calibration unit, the electric energy metering unit and the CF pulse generating unit, and is used to calibrate the metering accuracy of the electric energy metering unit, the meter difference calibration unit and the parameters of the CF pulse generating unit; The power and effective value metering unit is used to re-measure the electric energy before the difference calibration unit, the electric energy metering unit and the CF pulse unit calibration; The data storage device is used to store the output data of the electric energy metering unit, the CF pulse generating unit, and the power and effective value metering unit, and is connected to the distributed IO interface of the microprocessor.

6. The high-precision IoT smart meter for searching and measuring electric energy parameters according to claim 5 is characterized in that: Also includes: A wavelet transform module is used to perform wavelet transform on the sinusoidal voltage signal output by the contrast difference calibration unit, identify harmonics and interharmonics, and obtain voltage signals and current signals of harmonics and interharmonics; The frequency sorting unit is used to perform frequency sorting on the voltage and current signals of the harmonics and interharmonics output by the wavelet transform module and then calculate the average value, obtain the average value of the voltage and current signals of the harmonics and interharmonics, and send the output to the high-pass filter for high-pass filtering. After filtering by the high-pass filter, the signals are input into the electric energy metering module for harmonic and interharmonic electric energy metering, and the electric energy calculated by the high-pass filter in the electric energy metering module with the sinusoidal voltage and current signals output by the ratio difference calibration unit is summed to obtain the total electric energy to be measured.

7. The high-precision IoT smart meter for searching and measuring electric energy parameters according to claim 5 is characterized in that: Also includes: A standby function measurement channel, one output of the standby function measurement channel is combined with the output of the temperature sensor and output to the distributed IO interface of a microprocessor such as a DSP, so that temperature control can be performed when the standby function measurement channel is used, so that the activation of the standby function measurement channel does not affect the normal operation of the high-precision IoT smart meter for searching and measuring electric energy parameters; another output of the standby function measurement channel is output to a comparator, the comparator outputs to a modulator, the modulator is connected to the output of a phase-locked loop PLL, and the comparator and the modulator are used to modulate the signal input to the standby function test channel; The phase-locked loop PLL is used to phase-lock the real-time clock provided by the high-frequency crystal oscillator or the quartz crystal oscillator, the output of the SVM search synthesizer in each identification and measurement circuit, and the output of the modulator.

8. A distributed solar energy storage and charging / discharging system based on electric energy parameter search and measurement of high-precision IoT smart meters, characterized in that: include: Distributed photovoltaic inverter power generation system, used for photovoltaic power generation; BMS-Battery Management System, BMS-Battery Management System is used to intelligently manage and maintain the battery pack, and use the battery pack for charging and discharging, and is connected to the first transformer through the battery pack and AC / DC PCS subsystem connected in sequence, and the first transformer is connected to the smart grid; Photovoltaic storage charging and discharging DC cabinet is used to conduct convergence and lightning protection of the DC output of the distributed photovoltaic inverter power generation system, and to charge the battery pack of the BMS-battery management system under the control of the intelligent control manager; Intelligent control manager, used to manage and control the charge and discharge of battery packs of BMS-battery management system; The metering lightning protection main distribution box is used to provide overvoltage and undervoltage protection, lightning protection and electric energy metering for the output of the distributed photovoltaic inverter power generation system. The output end of the metering lightning protection main distribution box is connected to the three-phase four-wire mains power supply; The second smart meter adopts the high-precision IoT smart meter for searching and measuring electric energy parameters as described in any one of claims 5 to 7, and is arranged on the grid side of the access point where the main lightning protection distribution box is connected to the main power supply, and is used to accurately measure the electric energy supplied by the smart grid to the load.

9. A distributed solar energy storage and charging / discharging system based on electric energy parameter search and measurement of high-precision IoT smart meters according to claim 8, characterized in that: The metering lightning protection main distribution box includes a third circuit breaker, an over / under voltage protection circuit, a circuit breaker, a surge protection circuit, a knife switch, a connection terminal, and a first smart meter, wherein: The first smart meter adopts the electric energy parameter search and measurement high-precision IoT smart meter as claimed in any one of claims 5 to 7; One end of the third circuit breaker is connected to the AC output end of the distributed photovoltaic inverter power generation system, and the other end of the third circuit breaker is connected to one end of the over-voltage and under-voltage protection circuit; the other end of the over-voltage and under-voltage protection circuit is connected in two ways, one of which is connected to one end of the circuit breaker, the other end of the circuit breaker is connected to the surge protection circuit, and the other is connected to one end of the knife switch, and the other end of the knife switch is connected to the first smart meter via the connection terminal; The first smart meter is connected to the three-phase four-wire mains via a three-phase composite switch, and the load side of the access point is connected to the three-phase load via a three-phase switch; The second smart electric meter is connected to the smart grid via a second transformer.

10. A distributed solar energy storage and charging / discharging system based on electric energy parameter search and measurement of high-precision IoT smart meters according to claim 8, characterized in that: Also includes: The anti-reverse current controller is used to detect the power grid to determine whether there is reverse current when the distributed photovoltaic inverter power generation system is generating electricity, and to control the system to make the reverse current meet the requirements when the reverse current exceeds the requirements; The intelligent control manager comprises: EMS monitoring and management host protection system, used to monitor and manage multiple BMS battery management systems; MPPT controller, used for maximum power point tracking control; PWM controller, used to control the inverter or rectification of the AC / DC PCS subsystem connected to the battery pack in each BMS-battery management system; Each BMS-battery management system is also connected to the temperature monitoring system and fire protection system, including: The temperature monitoring system is used to monitor the temperature of the battery pack; The fire protection system is used to prevent the battery pack of the BMS-battery management system from catching fire or exploding; The photovoltaic storage charging and discharging DC cabinet comprises: Combiner box, used to combine the DC output of distributed photovoltaic inverter power generation system; A first circuit breaker, used for switching the output of the combiner box; Lightning arrester, used to control the total current between the combiner box and the smart manager; An ammeter and a voltmeter, used to detect the voltage and current of the total current between the combiner box and the smart manager; The second circuit breaker is used to control the total current between the photovoltaic storage charging and discharging DC cabinet and the intelligent manager.

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